Bioinformatics - Computational Cell Biology WW

487 Pages·2020·6.72 MB·English

i

Note to Springer: there is a Þgure that goes here (commented out in the Tex) that I
can’t get to compile.

Joel Edward Keizer 1942-1999

Joel Keizer’s thirty years of scientiÞc work set a standard for collaborative research
in theoretical chemistry and biology. Joel Keizer served the University of California at
Davis faithfully for 28 years, as a Professor in the Departments of Chemistry and of
Neurobiology, Physiology and Behavior, and as founder and Director of the Institute
for Theoretical Dynamics. Standing at the boundary between experiment and theory,
Joel built networks of collaborations and friendships that continue to grow and produce
results. This book evolved from a textbook that Joel began but was not able to Þnish.
The general outline and goals of the book were laid out by Joel, on the basis of his
many years of teaching and research in computational cell biology. Those of us who
helped to Þnish the project—as authors and editors—are happy to dedicate our labors
to the memory of our friend and colleague, Joel Edward Keizer. All royalties from
this book are to be directed to the Joel E. Keizer Memorial Fund for collaborative
interdisciplinary research in the life sciences.

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Preface

This text is an introduction to dynamical modeling in molecular cell biology. It is
not meant as a complete overview of modeling or of particular models in cell biology.
Rather, we use selected biological examples to motivate the concepts and techniques
used in computational cell biology. This is done through a progression of increasingly
more complex cellular functions modeled with increasingly complex mathematical and
computational techniques.
There are other excellent sources for material on mathematical cell biology, and
so the focus here truly is computer modeling. This does not mean that there are no
mathematical techniques introduced, because some of them are absolutely vital, but it
does mean that much of the mathematics is explained in a more intuitive fashion, while
we allow the computer to do most of the work. No former programming experience is
necessary, though basic programming experience and familiarity with computers will
be very helpful.
The target audience for this text is mathematically sophisticated cell biology or
neuroscience students or mathematics students who wish to learn about modeling in
cell biology. The ideal class would comprise both biology and applied math students,
who might be encouraged to collaborate on exercises or class projects. We assume as
little mathematical and biological background as we feel we can get away with, and
we proceed fairly slowly. The techniques and approaches covered in the Þrst half of the
book will form a basis for some elementary modeling or as a lead in to more advanced
topics covered in the second half of the book. Our goal for this text is to encourage
mathematics students to consider collaboration with experimentalists and to provide
students in cell biology and neuroscience with the tools necessary to access the modeling
literature and appreciate the value of theoretical approaches.

Preface
iii

The core of this book is a set of notes for a textbook written by Joel Keizer be-
fore his death in 1999. In addition to many other accomplishments as a scientist, Joel
founded and directed the Institute of Theoretical Dynamics at the University of Cal-
ifornia, Davis. It is currently the home of a training program for young scientists in
nonlinear dynamics in biology, funded by the National Science Foundation. As a part of
this training program Joel taught a course entitled “Computational Models of Cellular
Signaling,” which covered much of the material in the Þrst half of this book.
Joel took palpable joy from interaction with his colleagues, and in addition to his
truly notable accomplishments as a theorist in both chemistry and biology, perhaps his
greatest skill was his ability to bring diverse people together in successful collaboration.
It is in recognition of this gift that Joel’s friends and colleagues have brought this
text to completion. We have expanded the scope, but at the core, you will still Þnd
Joel’s hand in the approach, methodology, and commitment to the interdisciplinary
and collaborative nature of the Þeld. The royalties from the book will be donated to
the Joel E. Keizer foundation at the University of California at Davis, which promotes
interdisciplinary collaboration between mathematics, the physical sciences, and biology.
Audience: We have aimed this text at an advanced undergraduate or beginning
graduate audience in either mathematics or biology.
Prerequisites: We assume that students have taken full—year courses in calculus
and biology. Introductory courses in differential equations and molecular cell biology
are desirable but not absolutely necessary. Studens with more substantial background
in either biology or mathematics will beneÞt all the more from this text, especially the
second half. No former programming experience is needed, but a working knowledge of
using computers will make the learning curve much more pleasant. Note that we often
point students to other textbooks and monographs, both because they are important
references for later use and because they might be a better source for the material.
Instructors may want to have these sources available for students to borrow or consult.
Organization: We consider the Þrst six chapters, through intercellular communi-
cation, to be the core of the text. They cover the basic elements of compartmental
modeling, and they should be accessible to anyone with a minimum background in cell
biology and calculus. The remainder of the chapters cover more specialized topics that
can be selected from, based on the focus of the course. Chapters 7 and 8 introduce spa-
tial modeling, Chapters 9 and 10 discuss biochemical oscillations and the cell cycle, and
Chapters 11—13 cover stochastic methods and models. These chapters are of varying
degrees of difficulty.
Finally, in the Þrst appendix, some of the mathematical and computational con-
cepts brought up throughout the book are covered in more detail. This appendix is
meant to be a reference and a learning tool. Sections of it may be integrated into the
chapters as the topics are introduced. The second appendix contains an introduction to
the XPPAUT ODE package discussed below. The Þnal appendix contains psuedocode
versions of the code used to create some of the data Þgures in the text.
Internet Resources: This book will have its own web page, supported by Springer-
Verlag, which will contain a variety of resources. We will maintain a list of the inevitable

iv
Preface

mistakes and typos and will make available actual code for the Þgures in the book. A
solutions manual is being considered.
Software: We designed the text to be independent of any particular software, but
have included appendices in support of the XPPAUT package. XPPAUT has been
developed by Bard Ermentrout at the University of Pittsburgh, and it is currently
available free of charge. XPPAUT numerically solves and plots the solutions of ordinary
differential equations. It also incorporates a numerical bifurcation software and some
methods for stochastic equations. Versions are currently available for Windows, Linux,
and Unix systems. Recent changes in the Macintosh platform (OSX) make it possible
to use XPP there as well. Ermentrout has recently published an excellent user’s manual
available through SIAM [Ermentrout, 2002].
There are a large number of other software packages available that can accomplish
many of the same things as XPPAUT can, such as MATLAB, MapleV, Mathemat-
ica, and Berkeley Madonna. Programming in C or Fortran is also possible. However
XPPAUT is easy to use, requires minimal programming skills, has an excellent online
tutorial, and is distributed without charge. The aspect of XPPAUTwhich is available
in very few other places is the bifurcation software AUTO. The bifurcation tools in
XPPAUT are necessary only for selected problems, so many of the other packages will
suffice for most of the book. The the book and web site contain code that will repro-
duce many of the Þgures in the book. As students solve the exercises and replicate the
simulations using other packages, we would encourage the submission of the code to
the editors. We will incorporate this code into the web site and possibly into future
editions of the book.
There are many people to thank for their help with this project. Of course, we
are deeply indebted to the contributors, who Þrst completed or wrote from scratch the
chapters and then dealt with the numerous revisions necessary to homogenize the book
to a reasonable level. Carla Wofsy and Byron Goldstein, as well as Albert Goldbeter,
encouraged us to go forward with the project and provided valuable suggestions. We
thank Chris Dugaw and David Quinonez for their assistance with typesetting several
of the chapters, and Randy Szeto for his work with the graphic design of the book.
We thank James Sneyd for many helpful comments on the manuscript, and also Tim
Lewis for commenting on several of the chapters. Carol Lucas generously provided many
corrections for the Þrst half of the text. C.F., J.W., and E.M. were supported in part
by the Institute of Theoretical Dynamics at UC Davis during some of the preparation
of the manuscript.
We suspect that Joel, for a start, would have thanked Lee Segel, Jim Murray,
Leah Edelstein-Keshet and others whose pioneering textbooks in mathematical biology
certainly informed this one. We know that Joel would have thanked many friends and
colleagues for contributing to the true excitement he felt in his “second career” studying
biology. While we have dedicated this work to the memory of Joel, Joel’s dedication
might well have been to his wife, Susan; his daughter, Sarah; his son and daughter-in-
law, Sidney and Noelle; and his grandson, Justin Joel.

Preface
v

We hope you enjoy this text, and we look forward to your comments and sugges-
tions. We strongly believe that a textbook such as this might serve to help to develop
the Þeld of computational cell biology by introducing students to the subject. This
textbook will be more successful in helping to forge a community if it represents what
most of us agree is necessary to teach beginning students. This is only a Þrst step,
and we truly look forward both to input about the material already presented and to
suggestions and contributions of additional material and topics for future editions.

vi
Preface

Contributors
Timothy C. Elston
North Carolina State University
Department of Statistics

G. Bard Ermentrout
University of Pittsburgh
Department of Mathematics

Christopher P. Fall
New York University
Center for Neural Science
Courant Institute of Mathematical Science

James P. Keener
University of Utah
Department of Mathematics

Joel E. Keizer
University of California at Davis
Institute of Theoretical Dynamics

Yue-Xian Li
University of British Columbia
Department of Mathematics

Eric Marland
Appalachian State University
Department of Mathematical Sciences

Alexander Mogilner
University of California at Davis
Department of Mathematics

B´ela Nov´ak
Technical University of Budapest
Department of Agricultural Chemical
Technology

George Oster
University of California at Berkeley
Departments of Molecular and Cellular Biology
and ESPM

John E. Pearson
Los Alamos National Laboratory
Applied Theoretical and Computational Physics

John Rinzel
New York University
Center for Neural Science and
Courant Institute of Mathematical Science

Arthur S. Sherman
National Institutes of Health
Mathematical Research Branch
National Institute of Diabetes and
Digestive and Kidney Diseases

Gregory D. Smith
College of William and Mary
Department of Applied Science

John J. Tyson
Virginia Polytechnic Institute
and State University
Department of Biology

John M. Wagner
University of Connecticut Health Center
Center for Biomedical Imaging Technology

Hongyun Wang
University of California at Santa Cruz
Department of Applied Mathematics
and Statistics

Graphic design by
Randy Szeto

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Contents

Preface
ii

I
PART I - Introductory Course
1

1
Dynamic Phenomena in Cells
3
1.1
Scope of Cellular Dynamics . . . . . . . . . . . . . . . . . . . . . . . . .
3
1.2
Computational Modeling in Biology . . . . . . . . . . . . . . . . . . . .
7
1.2.1
Cartoons, Mechanisms, and Models . . . . . . . . . . . . . . . .
8
1.2.2
The Role of Computation
. . . . . . . . . . . . . . . . . . . . .
9
1.2.3
The Role of Mathematics . . . . . . . . . . . . . . . . . . . . . .
10
1.3
A Simple Molecular Switch . . . . . . . . . . . . . . . . . . . . . . . . .
11
1.4
Solving and Analyzing Differential Equations
. . . . . . . . . . . . . .
13
1.4.1
Numerical Integration of Differential Equations . . . . . . . . .
15
1.4.2
Introduction to Numerical Packages . . . . . . . . . . . . . . . .
18

2
Voltage Gated Ionic Currents
21
2.1
Basis of the Ionic Battery . . . . . . . . . . . . . . . . . . . . . . . . . .
23
2.1.1
The Nernst Potential: Charge Balances Concentration . . . . .
24
2.1.2
The Resting Membrane Potential . . . . . . . . . . . . . . . . .
25
2.2
The Membrane Model . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26
2.2.1
Equations for Membrane Electrical Behavior
. . . . . . . . . .
27
2.3
Activation and Inactivation Gates . . . . . . . . . . . . . . . . . . . . .
29
2.3.1
Models of Voltage—Dependent Gating . . . . . . . . . . . . . . .
29
2.3.2
The Voltage Clamp . . . . . . . . . . . . . . . . . . . . . . . . .
31

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Contents

2.4
Interacting Ion Channels: The Morris—Lecar Model . . . . . . . . . . .
34
2.4.1
Phase Plane Analysis . . . . . . . . . . . . . . . . . . . . . . . .
37
2.4.2
Stability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . .
38
2.4.3
Why Do Oscillations Occur? . . . . . . . . . . . . . . . . . . . .
40
2.4.4
Excitability and Action Potentials . . . . . . . . . . . . . . . . .
43
2.4.5
Type I and Type II Spiking
. . . . . . . . . . . . . . . . . . . .
44
2.5
The Hodgkin—Huxley Model . . . . . . . . . . . . . . . . . . . . . . . .
45
2.6
FitzHugh—Nagumo Class Models . . . . . . . . . . . . . . . . . . . . . .
48
2.7
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49

3
Transporters and Pumps
53
3.1
Passive Transport
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
54
3.2
Transporter Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
57
3.2.1
Algebraic Method . . . . . . . . . . . . . . . . . . . . . . . . . .
59
3.2.2
Diagrammatic Method . . . . . . . . . . . . . . . . . . . . . . .
60
3.2.3
Rate of the GLUT Transporter
. . . . . . . . . . . . . . . . . .
62
3.3
The Na+/Glucose Cotransporter . . . . . . . . . . . . . . . . . . . . . .
65
3.4
SERCA Pumps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
70
3.5
Transport Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
73

4
Reduction of Scale
77
4.1
The Rapid Equilibrium Approximation . . . . . . . . . . . . . . . . . .
78
4.2
Time—Scale Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
82
4.3
Glucose—Dependent Insulin Secretion . . . . . . . . . . . . . . . . . . .
83
4.4
Ligand Gated Channels . . . . . . . . . . . . . . . . . . . . . . . . . . .
88
4.5
The Neuromuscular Junction . . . . . . . . . . . . . . . . . . . . . . . .
90
4.6
The Inositol Trisphosphate (IP3) receptor
. . . . . . . . . . . . . . . .
91
4.7
Michaelis—Menten Kinetics . . . . . . . . . . . . . . . . . . . . . . . . .
94

5
Whole—Cell Models
101
5.1
Models of ER and PM Calcium Handling . . . . . . . . . . . . . . . . .
102
5.1.1
Flux Balance Equations with Rapid Buffering . . . . . . . . . .
103
5.1.2
Expressions for the Fluxes . . . . . . . . . . . . . . . . . . . . .
106
5.2
Calcium Oscillations in the Bullfrog Sympathetic Ganglion Neuron . .
108
5.2.1
Ryanodine Receptor Kinetics: The Keizer—Levine Model . . . .
109
5.2.2
Bullfrog Sympathetic Ganglion Neuron Closed—Cell Model
. .
111
5.2.3
Bullfrog Sympathetic Ganglion Neuron Open—Cell Model . . .
112
5.3
The Pituitary Gonadotroph . . . . . . . . . . . . . . . . . . . . . . . . .
115
5.3.1
The ER Oscillator in a Closed Cell . . . . . . . . . . . . . . . .
116
5.3.2
Open—Cell Model with Constant Calcium Inßux
. . . . . . . .
122
5.3.3
The Plasma Membrane Oscillator . . . . . . . . . . . . . . . . .
124
5.3.4
Bursting Driven by the ER in the Full Model . . . . . . . . . .
126
5.4
The Pancreatic Beta Cell . . . . . . . . . . . . . . . . . . . . . . . . . .
128

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ix

5.4.1
Chay—Keizer Model . . . . . . . . . . . . . . . . . . . . . . . . .
130
5.4.2
Chay—Keizer with an ER . . . . . . . . . . . . . . . . . . . . . .
133

6
Intercellular Communication
140
6.1
Electrical Coupling and Gap Junctions . . . . . . . . . . . . . . . . . .
142
6.1.1
Synchronization of Two Oscillators . . . . . . . . . . . . . . . .
142
6.1.2
Asynchrony Between Oscillators . . . . . . . . . . . . . . . . . .
143
6.1.3
Cell Ensembles, Electrical Coupling Length Scale . . . . . . . .
144
6.2
Synaptic Transmission Between Neurons . . . . . . . . . . . . . . . . .
146
6.2.1
Kinetics of Postsynaptic Current
. . . . . . . . . . . . . . . . .
147
6.2.2
Synapses: Excitatory and Inhibitory; Fast and Slow
. . . . . .
148
6.3
When Synapses Might (or Might Not) Synchronize Active Cells . . . .
150
6.4
Neural Circuits as Computational Devices . . . . . . . . . . . . . . . .
154
6.5
Large—Scale Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . .
159

II
PART II - Advanced Material
169

7
Spatial Modeling
171
7.1
One-Dimensional Formulation . . . . . . . . . . . . . . . . . . . . . . .
173
7.1.1
Conservation in One Dimension . . . . . . . . . . . . . . . . . .
173
7.1.2
Fick’s Law of Diffusion . . . . . . . . . . . . . . . . . . . . . . .
175
7.1.3
Advection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
176
7.1.4
Flux of Ions in a Field
. . . . . . . . . . . . . . . . . . . . . . .
177
7.1.5
The Cable Equation . . . . . . . . . . . . . . . . . . . . . . . . .
177
7.1.6
Boundary and Initial Conditions
. . . . . . . . . . . . . . . . .
178
7.2
Important Examples with Analytic Solutions . . . . . . . . . . . . . . .
179
7.2.1
Diffusion Through a Membrane . . . . . . . . . . . . . . . . . .
179
7.2.2
Ion Flux Through a Channel . . . . . . . . . . . . . . . . . . . .
180
7.2.3
Voltage Clamping . . . . . . . . . . . . . . . . . . . . . . . . . .
181
7.2.4
Diffusion in a Long Dendrite . . . . . . . . . . . . . . . . . . . .
181
7.2.5
Diffusion into a Capillary . . . . . . . . . . . . . . . . . . . . . .
183
7.3
Numerical Solution of the Diffusion Equation
. . . . . . . . . . . . . .
184
7.4
Multidimensional Problems . . . . . . . . . . . . . . . . . . . . . . . . .
186
7.4.1
Conservation Law in Multiple Dimensions . . . . . . . . . . . .
186
7.4.2
Fick’s Law in Multiple Dimensions . . . . . . . . . . . . . . . .
187
7.4.3
Advection in Multiple Dimensions . . . . . . . . . . . . . . . . .
188
7.4.4
Boundary and Initial Conditions for Multiple Dimensions . . .
188
7.4.5
Diffusion in Multiple Dimensions: Symmetry
. . . . . . . . . .
188
7.5
Traveling Waves in Nonlinear Reaction—Diffusion Equations . . . . . .
189
7.5.1
Traveling Wave Solutions . . . . . . . . . . . . . . . . . . . . . .
190
7.5.2
Traveling Wave in the Fitzhugh—Nagumo Equations . . . . . .
193

x
Contents

8
Modeling Intracellular Calcium Waves and Sparks
198
8.1
Microßuorometric Measurements . . . . . . . . . . . . . . . . . . . . . .
198
8.2
A Model of the Fertilization Calcium Wave . . . . . . . . . . . . . . . .
200
8.3
Including Calcium Buffers in Spatial Models . . . . . . . . . . . . . . .
202
8.4
The Effective Diffusion Coefficient . . . . . . . . . . . . . . . . . . . . .
203
8.5
Simulation of a Fertilization Calcium Wave . . . . . . . . . . . . . . . .
204
8.6
Simulation of a Traveling Front
. . . . . . . . . . . . . . . . . . . . . .
205
8.7
Calcium Waves in the Immature Xenopus Oocycte . . . . . . . . . . .
208
8.8
Simulation of a Traveling Pulse
. . . . . . . . . . . . . . . . . . . . . .
208
8.9
Simulation of a Kinematic Wave . . . . . . . . . . . . . . . . . . . . . .
210
8.10
Spark-Mediated Calcium Waves . . . . . . . . . . . . . . . . . . . . . .
212
8.11
The Fire—Diffuse—Fire Model . . . . . . . . . . . . . . . . . . . . . . . .
215
8.12
Modeling Localized Calcium Elevations . . . . . . . . . . . . . . . . . .
220
8.13
Steady-State Localized Calcium Elevations . . . . . . . . . . . . . . . .
223
8.13.1 The Steady—State Excess Buffer Approximation (EBA) . . . .
224
8.13.2 The Steady—State Rapid Buffer Approximation (RBA)
. . . .
225
8.13.3 Complementarity of the Steady-State EBA and RBA
. . . . .
226

9
Biochemical Oscillations
231
9.1
Biochemical Kinetics and Feedback . . . . . . . . . . . . . . . . . . . .
233
9.2
Regulatory Enzymes . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
237
9.3
Two-Component Oscillators Based on Autocatalysis
. . . . . . . . . .
240
9.3.1
Substrate—Depletion Oscillator . . . . . . . . . . . . . . . . . . .
241
9.3.2
Activator—Inhibitor Oscillator . . . . . . . . . . . . . . . . . . .
243
9.4
Three-Component Networks Without Autocatalysis . . . . . . . . . . .
244
9.4.1
Positive Feedback Loop and the Routh—Hurwitz Theorem . . .
245
9.4.2
Negative Feedback Oscillations
. . . . . . . . . . . . . . . . . .
245
9.4.3
The Goodwin Oscillator
. . . . . . . . . . . . . . . . . . . . . .
246
9.5
Time-Delayed Negative Feedback
. . . . . . . . . . . . . . . . . . . . .
248
9.5.1
Distributed Time Lag and the Linear Chain Trick
. . . . . . .
249
9.5.2
Discrete Time Lag . . . . . . . . . . . . . . . . . . . . . . . . . .
250
9.6
Circadian Rhythms
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
251

10 Cell Cycle Controls
263
10.1
Physiology of the Cell Cycle in Eukaryotes . . . . . . . . . . . . . . . .
263
10.2
Molecular Mechanisms of Cell Cycle Control . . . . . . . . . . . . . . .
265
10.3
A Toy Model of Start and Finish
. . . . . . . . . . . . . . . . . . . . .
267
10.3.1 Hysteresis in the Interactions Between Cdk and APC
. . . . .
268
10.3.2 Activation of the APC at Anaphase . . . . . . . . . . . . . . . .
270
10.4
A Serious Model of the Budding Yeast Cell Cycle . . . . . . . . . . . .
271
10.5
Cell Cycle Controls in Fission Yeast . . . . . . . . . . . . . . . . . . . .
274
10.6
Checkpoints and Surveillance Mechanisms . . . . . . . . . . . . . . . .
277
10.7
Division Controls in Egg Cells . . . . . . . . . . . . . . . . . . . . . . .
279